ALADIN-based Distributed Model Predictive Control with dynamic partitioning: An application to Solar Parabolic Trough Plants

TL;DR

This paper introduces a distributed model predictive control method using ALADIN with dynamic partitioning to efficiently manage temperature control in solar parabolic trough plants, reducing computational complexity while maintaining performance.

Contribution

It presents a novel combination of ALADIN-based distributed MPC with dynamic clustering for solar plant control, addressing nonlinearities and coupled constraints effectively.

Findings

Successful application to a 10-loop solar plant model.

Reduced computational complexity with minimal performance loss.

Demonstrated effectiveness of dynamic partitioning in control performance.

Abstract

This article presents a distributed model predictive controller with time-varying partitioning based on the augmented Lagrangian alternating direction inexact Newton method (ALADIN). In particular, we address the problem of controlling the temperature of a heat transfer fluid (HTF) in a set of loops of solar parabolic collectors by adjusting its flow rate. The control problem involves a nonlinear prediction model, decoupled inequality constraints, and coupled affine constraints on the system inputs. The application of ALADIN to address such a problem is combined with a dynamic clustering-based partitioning approach that aims at reducing, with minimum performance losses, the number of variables to be coordinated. Numerical results on a 10-loop plant are presented.